Evaluasi Aplikasi Raileo Melalui Analisis Sentimen Ulasan Playstore Dengan Metode Naive Bayes

Haris Junianto; Primandani  Arsi; Bagus Adhi  Kusuma; Dhanar Intan Surya  Saputra

doi:10.31598/sintechjournal.v7i1.1505

Authors

Haris Junianto Universitas Amikom Purwokerto
Primandani Arsi Universitas Amikom Purwokerto
Bagus Adhi Kusuma Universitas Amikom Purwokerto
Dhanar Intan Surya Saputra Universitas Amikom Purwokerto

DOI:

https://doi.org/10.31598/sintechjournal.v7i1.1505

Keywords:

ulasan, aplikasi, raileo, NB, sentimen

Abstract

Abstrak

The Raileo application is a staffing platform owned by PT. KAI, functions as a personnel data management system. Effective application development requires data as a basis, and one source of data that can be utilized is user reviews. User reviews provide valuable information regarding application performance, user needs, and security aspects. However, challenges arise in managing review data which often contains sarcasm, creating ambiguous meaning and lowering accuracy levels. This research proposes a solution by applying sentiment analysis using Naive Bayes logarithms to 1047 Raileo review data. This method produces an accuracy rate of 94%, with positive and negative sentiment classification. The research results show the words that appear most frequently in Raileo reviews, such as "eror", "sulit", "titik presensi", "titik absen", "titik lokasi", "bug", "lemot," "gagal", "mantap", "bagus", "oke", "mudah", "mempermudah", "mantul", "lengkap","keren","ok", "inovatif", "inovasi", "semoga", "sukses", dan "membantu". These words can be used as a key to analyze all the sentiments contained in the review. In addition, this research identifies "presence point" as the highest negative sentiment word that needs attention in further development. From this sentiment analysis research, the Raileo application produces the highest sentiment value, namely positive sentiment

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Evaluasi Aplikasi Raileo Melalui Analisis Sentimen Ulasan Playstore Dengan Metode Naive Bayes

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